Self-supervised learning refers to training with pseudo labels where pseudo labels are automatically generated for a pre-defined pretext task without involving any human annotations. Chen et al. in SSTDA [1]} proposed self-supervised temporal domain adaptation method to address the spatio-temporal variations (different people performing the tasks in different styles) in action segmentation. They designed two self-supervised auxiliary tasks, binary and sequential domain prediction, to jointly align local and global embedded feature spaces across domains. The binary domain prediction task predicts a single domain for each frame-level feature, and the sequential domain prediction task predicts the permutation of domains for an untrimmed video, both trained by adversarial training with a gradient reversal layer (GRL) [2]}, [3]}. Jain et al. in Actionbytes [4]} only use short trimmed videos during the training and train an action localization network with cluster assignments as pseudo-labels to segments a long untrimmed videos into interpretable fragments (called ActionBytes). They adopt a self-supervised iterative approach for training boundary-aware models from short videos by decomposing a trimmed video into ActionBytes and generate pseudo-labels to train a CNN to localize ActionBytes within videos.
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